Mythosia.VectorDb.Qdrant 1.0.0

Mythosia.VectorDb.Qdrant

Qdrant vector store implementation for the Mythosia VectorDb abstraction layer.

Uses a single Qdrant collection (physical container) with payload-based logical isolation:

• _namespace — first-tier logical partition
• _scope — second-tier logical partition

Installation

dotnet add package Mythosia.VectorDb.Qdrant

Quick Start

using Mythosia.VectorDb;
using Mythosia.VectorDb.Qdrant;

// 1. Configure — CollectionName is the physical Qdrant collection
var options = new QdrantOptions
{
    Host           = "localhost",
    Port           = 6334,
    CollectionName = "my_vectors",                  // physical collection
    Dimension      = 1536,                          // must match your embedding model
    DistanceStrategy = QdrantDistanceStrategy.Cosine
};

// 2. Create the store
using var store = new QdrantStore(options);

// 3. Upsert records — "documents" is a logical namespace within the collection
var record = new VectorRecord("doc-1", embedding, "Hello world");
await store.InNamespace("documents").UpsertAsync(record);

// 4. Search
var results = await store.InNamespace("documents")
    .SearchAsync(queryVector, topK: 5);

Options

Scope & Metadata Filtering

// Scope isolation (2nd-tier within namespace)
await store.InNamespace("docs").InScope("tenant-1").UpsertAsync(record);
var results = await store.InNamespace("docs").InScope("tenant-1")
    .SearchAsync(queryVector, topK: 10);

// Metadata filtering
var filter = VectorFilter.ByMetadata("category", "science");
var results = await store.InNamespace("docs")
    .SearchAsync(queryVector, topK: 5, filter: filter);

// Minimum score threshold
var filter = new VectorFilter { MinScore = 0.7 };
var results = await store.InNamespace("docs")
    .SearchAsync(queryVector, topK: 5, filter: filter);

Advanced: Inject a Pre-configured Client

using Qdrant.Client;

var client = new QdrantClient("my-qdrant-cloud.example.com", 6334, https: true, apiKey: "my-key");
var store = new QdrantStore(options, client);
// The caller is responsible for disposing the QdrantClient.

Payload Layout

Records are stored as Qdrant points with the following payload keys:

ID Mapping

Point IDs are deterministic UUIDs derived from namespace + record Id (when namespace is set) or just record Id (when null) via MD5 hash. This ensures the same record Id in different namespaces produces distinct points within the shared collection. The original string ID is preserved in the _id payload field.

License

See repository root for license information.